Predicting the future value of gold: A time series approach

Abstract

Time series analysis has become an invaluable tool in the realm of financial markets, particularly when it comes to predicting the future value of gold. In addition to conventional methods like technical and fundamental analysis, recent advancements in time series algorithms have opened up new avenues for leveraging technological progress. By employing these techniques, investors can gain valuable insights and make well-informed decisions regarding their gold investments.

One promising approach involves developing a technical analysis tool that utilizes past market data to forecast the future trajectory of gold prices. This tool will rely on time series analysis, which involves analyzing historical price data to identify patterns, trends, and cycles. By understanding these patterns and trends, investors can anticipate potential future movements in the value of gold.

The tool will utilize various time series algorithms to extract meaningful information from the historical data. These algorithms leverage statistical techniques and mathematical models to identify patterns and relationships within the data, enabling the prediction of future price movements. By incorporating these advanced algorithms, the tool aims to provide accurate and reliable forecasts, aiding investors in their decision-making processes.

Time series analysis offers valuable insights into predicting the future value of gold in the financial markets. Traditional approaches such as technical analysis and fundamental analysis continue to be used, while improvements in time series algorithms offer new possibilities by exploiting technological advances. As a results, we have developed a sophisticated technical analysis tool that utilizes historical market data to forecast the future trajectory of the gold price by employing a time series approach.

Keywords: time series, LSMA, trading, prediction, investment, gold forecast

JEL Classification: G11, G12, G13, G17
Paper type: Empirical research